In this directory, notebooks are provided to give a deep dive of collaborative filtering recommendation algorithms. The notebooks make use of the utility functions (recommenders) available in the repo.
| Notebook | Environment | Description |
|---|---|---|
| als_deep_dive | PySpark | Deep dive on the ALS algorithm and implementation. |
| baseline_deep_dive | --- | Deep dive on baseline performance estimation. |
| cornac_bivae_deep_dive | Python CPU, GPU | Deep dive on the BiVAE algorithm and implementation. |
| cornac_bpr_deep_dive | Python CPU | Deep dive on the BPR algorithm and implementation. |
| fm_deep_dive | Python CPU | Deep dive into factorization machine (FM) and field-aware FM (FFM) algorithm. |
| lightfm_deep_dive | Python CPU | Deep dive into matrix factorization model with LightFM. |
| lightgcn_deep_dive | Python CPU, GPU | Deep dive on a LightGCN algorithm and implementation. |
| multi_vae_deep_dive | Python CPU, GPU | Deep dive on the Multinomial VAE algorithm and implementation. |
| ncf_deep_dive | Python CPU, GPU | Deep dive on a NCF algorithm and implementation. |
| rbm_deep_dive | Python CPU, GPU | Deep dive on the rbm algorithm and its implementation. |
| sar_deep_dive | Python CPU | Deep dive on the SAR algorithm and implementation. |
| standard_vae_deep_dive | Python CPU, GPU | Deep dive on the Standard VAE algorithm and implementation. |
| surprise_svd_deep_dive | Python CPU | Deep dive on a SVD algorithm and implementation. |
Details on model training are best found inside each notebook.